1. Field
Certain embodiments of the present disclosure generally relate to neural system engineering and, more particularly, to a method and an apparatus for training of synapses in biologically inspired networks.
2. Background
In biologically inspired computing devices, communication between computational nodes (neurons) occurs through rates and relative timing of spikes. Functionality of a neural network can be represented by strengths of neuron-to-neuron connections referred to as synapses. These strengths or “synaptic weights” can be constantly adjusted by the network according to a relative timing between pre-synaptic and post-synaptic spiking.
Ideally, circuits for synaptic training are implemented such that a synaptic connection utilizes a minimal possible number of devices. This is because the number of synapses per neuron can be typically around 10,000 leading to a total number of synapses to 10 billion for a typical biological network of 1 million neurons.
A concept of one device per synapse trained by a pulse width modulation (PWM) signal has been proposed in the art for bio-inspired networks. However, a large number of channels are required for communicating between each pair of neurons. In addition, synaptic weights can be unintentionally changed outside weight-training events. These changes then need to be undone by applying opposite polarity training PWM signals. This, however, complicates implementation of the system, and leads to a high current and power consumption.